The setup process involves exporting emails to mbox format, setting up a Hugging Face account to download models, and having basic Python and Unix knowledge. The user's past emails are downloaded and used to create a training set. The model is then fine-tuned using the Robust Adaptation (RoSA) method. Once the custom user model is ready, Panza can be run locally with a RAG module. The assistant can be served in a friendly GUI or via a CLI. The creators also plan to release a CPU-only version and provide a Google Colab instance to try out Panza.
Key takeaways:
- Panza is a personal email assistant that can be trained and run entirely locally, ensuring user data privacy.
- The assistant uses a fine-tuned LLM that matches the user's writing style, and a Retrieval-Augmented Generation (RAG) component to produce relevant emails.
- Panza requires a single GPU with 16-24 GiB of memory for training and execution, and can generate a new email in a few seconds.
- The system is designed to be user-friendly, requiring only basic Python and Unix knowledge, and no prior experience with LLMs.